Large Healthcare Research Institution used Machine Learning to improve the physician’s authorization process.
“Once we understood the power of Machine Learning and its ability to detect images and also process text we felt comfortable that the solution will work.”
Claim Handling Specialist, Large Healthcare Research Institution
This large Healthcare Research Institution has experienced rapid growth in recent years, with their products and services used by over 3 million members. This growth meant that certain manual processes, such as physician’s authorizations processing, were slowing things down. They needed to be reassessed from the perspective of achieving breakthrough productivity. The client received faxed documents which were either printed or handwritten. The fax contained various forms, some relevant and some non-relevant. These could include Authorization Requests, Expedited Authorization Requests, Retro, Ad Hoc Ads, or more. From the fax documents, the client needed to identify relevant information, then classify whether to expedite the document.
The client’s current OCR based system, unfortunately, failed as it was unable to process whether the authorization notes from physicians were expedited or not. This is because the notes were often handwritten. By using Machine Learning, human decision making was reduced. ML and AI were able to determine the content of the faxed documents autonomously, being able to more accurately identify the contents of handwritten notes. This, in turn, helped drive faster processes and reduce lead time to act on the content of the fax. Ultimately, the ability to classify expedites became faster. Pluto7, with its GCP and Machine Learning expertise, determined that image processing, combined with natural language processing, could meet the challenge by classifying emergency and non-emergency requests correctly and transferring them to the right processing queues.
With these objectives in mind, this Healthcare Research Institution partnered with Pluto7 to transform their physician’s authorization processing. Faxed document data, fax volume, and the labor hours involved were analyzed to finalize the solution. Using Cloud Storage on Google Cloud Platform, Datalab for manipulating data, and Natural Language Processing (NLP), the proof of concept demonstrated that the solution met the desired requirement. This laid the foundation to enact the plan and roadmap for production implementation.
These improvements lead to perpetual savings, year over year. Working with Pluto7, this client was able to take their innovative mindset and apply it to their business, improving internal productivity and changing their members’ experience. By leveraging Google Cloud Platform and its components (Cloud Storage, AutoML Vision, TensorFlow, Data Studio and Machine Learning Engine) they were able to drive innovative process improvement, seeing tangible business benefits.
Working with Pluto7, this client was able to take their innovative mindset and apply it to their business, improving internal productivity and changing their members’ experience. By leveraging Google Cloud Platform and its components (Cloud Storage, AutoML Vision, TensorFlow, Data Studio and Machine Learning Engine) they were able to drive innovative process improvement, seeing tangible business benefits.
- Google Cloud Platform
- AutoML Vision
- Cloud Natural Language
- Google Cloud Storage
- Google Cloud Dataflow
- Google Machine Learning